一文详解如何实现PyTorch模型编译

pip install torch==1.7.0
pip install torchvision==0.8.1

import tvm
from tvm import relay
import numpy as np
from tvm.contrib.download import download_testdata
# 导入 PyTorch
import torch
import torchvision

model_name = "resnet18"
model = getattr(torchvision.models, model_name)(pretrained=True)
model = model.eval()
# 通过追踪获取 TorchScripted 模型
input_shape = [1, 3, 224, 224]
input_data = torch.randn(input_shape)
scripted_model = torch.jit.trace(model, input_data).eval()
输出结果：

from PIL import Image
img_url = "/file/imgs/upload/202305/17/jjohlw3owph.png?raw=true"
img_path = download_testdata(img_url, "cat.png", module="data")
img = Image.open(img_path).resize((224, 224))
# 预处理图像，并将其转换为张量
from torchvision import transfORMs
my_preprocess = transforms.Compose(
 [
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
 ]
)
img = my_preprocess(img)
img = np.expand_dims(img, 0)

input_name = "input0"
shape_list = [(input_name, img.shape)]
mod, params = relay.frontend.from_pytorch(scripted_model, shape_list)

target = tvm.target.Target("llvm", host="llvm")
dev = tvm.cpu(0)
with tvm.transform.PassContext(opt_level=3):
    lib = relay.build(mod, target=target, params=params)

from tvm.contrib import graph_executor
dtype = "float32"
m = graph_executor.GraphModule(lib["default"](dev))
# 设置输入
m.set_input(input_name, tvm.nd.array(img.astype(dtype)))
# 执行
m.run()
# 得到输出
tvm_output = m.get_output(0)

synset_url = "".join(
 [
 "https://raw.GitHubusercontent.com/Cadene/",
 "pretrained-models.pytorch/master/data/",
 "imagenet_synsets.txt",
 ]
)
synset_name = "imagenet_synsets.txt"
synset_path = download_testdata(synset_url, synset_name, module="data")
with open(synset_path) as f:
    synsets = f.readlines()
synsets = [x.strip() for x in synsets]
splits = [line.split(" ") for line in synsets]
key_to_classname = {spl[0]: " ".join(spl[1:]) for spl in splits}
class_url = "".join(
 [
 "Https://raw.githubusercontent.com/Cadene/",
 "pretrained-models.pytorch/master/data/",
 "imagenet_classes.txt",
 ]
)
class_name = "imagenet_classes.txt"
class_path = download_testdata(class_url, class_name, module="data")
with open(class_path) as f:
    class_id_to_key = f.readlines()
class_id_to_key = [x.strip() for x in class_id_to_key]
# 获得 TVM 的前 1 个结果
top1_tvm = np.argmax(tvm_output.numpy()[0])
tvm_class_key = class_id_to_key[top1_tvm]
# 将输入转换为 PyTorch 变量，并获取 PyTorch 结果进行比较
with torch.no_grad():
    torch_img = torch.from_numpy(img)
    output = model(torch_img)
 # 获得 PyTorch 的前 1 个结果
    top1_torch = np.argmax(output.numpy())
    torch_class_key = class_id_to_key[top1_torch]
print("Relay top-1 id: {}, class name: {}".format(top1_tvm, key_to_classname[tvm_class_key]))
print("Torch top-1 id: {}, class name: {}".format(top1_torch, key_to_classname[torch_class_key]))